Journal of Applied Mathematics
- J. Appl. Math.
- Volume 2014 (2014), Article ID 928051, 10 pages.
Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition
In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.
J. Appl. Math., Volume 2014 (2014), Article ID 928051, 10 pages.
First available in Project Euclid: 2 March 2015
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Yu, Zhe-Zhou; Liu, Yu-Hao; Li, Bin; Pang, Shu-Chao; Jia, Cheng-Cheng. Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition. J. Appl. Math. 2014 (2014), Article ID 928051, 10 pages. doi:10.1155/2014/928051. https://projecteuclid.org/euclid.jam/1425305772